With a wealth of enterprise-critical information being captured in natural language documentation of all forms, the problems with perusing only the top 10 or 20 most popular documents containing the user's two or three key words are becoming increasingly apparent. This is especially the case in the enterprise where popularity is not as important an indicator of relevance. The inventors in the present disclosure have recognized that enterprise computer systems should deeply analyze the breadth of relevant content to more precisely answer and justify answers to user's natural language questions. An open-domain Question Answering (QA) problem is one of the most challenging in the realm of computer science and artificial intelligence, requiring a synthesis of information retrieval, natural language processing, knowledge representation and reasoning, machine learning, and computer-human interfaces.
QA systems typically generate several potential candidate answers to a given question and use various algorithms to rank and score candidates based on their evidence. However, QA systems typically consider candidate answers independent of each other, and seldom, if ever, explore relationships among the candidates themselves.